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From Aljoscha Krettek <aljos...@apache.org>
Subject Re: Jobmanager HA with Rolling Sink in HDFS
Date Wed, 09 Mar 2016 12:31:25 GMT
Hi Maximilian,
I’m currently running some tests again on a cluster to try and pinpoint the problem. Just
to make sure, you are using Hadoop 2.4.1 with Yarn and Kafka 0.8, correct?

In the meantime, could you maybe run a test where you completely bypass Kafka, just so we
can see whether the problem is in Kafka or the RollingSink. For my tests I created this source:

public static class LongSource extends RichSourceFunction<Long> implements Checkpointed<Long>
{
   private static final long serialVersionUID = 1L;

   private long numElements;

   private int sleepInterval;

   private volatile boolean running = true;

   private long index = 0;

   public LongSource(long numElements, int sleepInterval) {
      this.numElements = numElements;
      this.sleepInterval = sleepInterval;
   }

   @Override
   public void run(SourceContext<Long> out) throws Exception {

      while (running && index < numElements) {
         out.collect(index);
         Thread.sleep(sleepInterval);
         index++;
      }

      while (running) {
         Thread.sleep(100);
      }
   }

   @Override
   public Long snapshotState(long l, long l1) throws Exception {
      return index;
   }

   @Override
   public void restoreState(Long aLong) throws Exception {
      this.index = aLong;
   }

   @Override
   public void cancel() {
      running = false;
   }
}

It’s a fault-tolerant source that emits elements and I can specify a sleep interval so that
the job is not too fast and I can kill it before it finishes.

My testing job is this, which should be quite similar to yours:

DataStream<Long> inputStream = env.addSource(new DataGenerator.LongSource(2000000, 1));

DataStream<String> result = inputStream.map(new RichMapFunction<Long, String>()
{
   LongCounter count;
   @Override
   public void open(Configuration parameters) throws Exception {
      count = getRuntimeContext().getLongCounter("count");
   }

   @Override
   public String map(Long aLong) throws Exception {
      count.add(1L);
      return "" + aLong;
   }
});

RollingSink<String> sink = new RollingSink<>(sinkPath);
sink.setBucketer(new NonRollingBucketer());
result.addSink(sink);

Cheers,
Aljoscha
> On 09 Mar 2016, at 08:31, Maximilian Bode <maximilian.bode@tngtech.com> wrote:
> 
> Hi Aljoscha,
> 
> yeah I should have been clearer. I did mean those accumulators but am not trusting them
in the sense of total number (as you said, they are reset on failure). On the other hand,
if they do not change for a while it is pretty obvious that the job has ingested everything
in the queue. But you are right, this is kind of heuristic. In combination with the fact that
the DateTimeBucketer does not create new folders I believe this should be sufficient to decide
when the job has basically finished, though.
> 
> So the setup is the following: The Flink job consists of a FlinkKafkaConsumer08, a map
containing just an IntCounter accumulator and finally a rolling sink writing to HDFS. I start
it in a per-job yarn session with n=3, s=4. Then I pour 2 million records in the Kafka queue
the application is reading from. If no job/task managers are killed, the behavior is exactly
as expected: the output files in HDFS grow with time and I can exactly monitor via the accumulator
when every record has been ingested from Kafka. After that time, I give the job a few seconds
and then cancel it via the web interface. Then still some time later (to give the job the
chance to output the few records still hanging around) a wc -l on the output files yields
exactly the expected 2 million.
> 
> On the other hand, if I kill a task manager while the job is in progress, one of the
12 output files seems to be missing as described before. A wc -l on only the relevant bytes
as I mentioned in an earlier mail then leads to a number smaller than 2 million.
> 
> We are using an FsStateBackend in HDFS with a checkpoint interval of 10s.
> 
> Cheers,
>  Max
> — 
> Maximilian Bode * Junior Consultant * maximilian.bode@tngtech.com
> TNG Technology Consulting GmbH, Betastr. 13a, 85774 Unterföhring
> Geschäftsführer: Henrik Klagges, Christoph Stock, Dr. Robert Dahlke
> Sitz: Unterföhring * Amtsgericht München * HRB 135082
> 
>> Am 08.03.2016 um 17:46 schrieb Aljoscha Krettek <aljoscha@apache.org>:
>> 
>> Hi,
>> with accumulator you mean the ones you get from RuntimeContext.addAccumulator/getAccumulator?
I’m afraid these are not fault-tolerant which means that the count in these probably doesn’t
reflect the actual number of elements that were processed. When a job fails and restarts the
accumulators should start from scratch. This makes me wonder how yours ever reach the required
2 mio, for it to be considered “done”.
>> 
>> This keeps getting more mysterious… 
>> 
>> By the way, what are you using as StateBackend and checkpoint interval?
>> 
>> Cheers,
>> Aljoscha
>>> On 08 Mar 2016, at 13:38, Maximilian Bode <maximilian.bode@tngtech.com>
wrote:
>>> 
>>> Hi,
>>> thanks for the fast answer. Answers inline.
>>> 
>>>> Am 08.03.2016 um 13:31 schrieb Aljoscha Krettek <aljoscha@apache.org>:
>>>> 
>>>> Hi,
>>>> a missing part file for one of the parallel sinks is not necessarily a problem.
This can happen if that parallel instance of the sink never received data after the job successfully
restarted.
>>>> 
>>>> Missing data, however, is a problem. Maybe I need some more information about
your setup:
>>>> 
>>>> - When are you inspecting the part files?
>>> Some time after the cluster is shut down
>>>> - Do you shutdown the Flink Job before checking? If so, how do you shut it
down.
>>> Via 'cancel' in the Jobmanager Web Interface. Some records seem to be written
only after cancelling the job, right?
>>>> - When do you know whether all the data from Kafka was consumed by Flink
and has passed through the pipeline into HDFS?
>>> I have an accumulator in a map right before writing into HDFS. Also, the RollingSink
has a DataTimeBucketer which makes it transparent when no new data is arriving anymore as
the last bucket is from some minutes ago.
>>>> 
>>>> Cheers,
>>>> Aljoscha
>>>>> On 08 Mar 2016, at 13:19, Maximilian Bode <maximilian.bode@tngtech.com>
wrote:
>>>>> 
>>>>> Hi Aljoscha,
>>>>> 
>>>>> oh I see. I was under the impression this file was used internally and
the output being completed at the end. Ok, so I extracted the relevant lines using
>>>>> 	for i in part-*; do head -c $(cat "_$i.valid-length" | strings) "$i"
> "$i.final"; done
>>>>> which seems to do the trick.
>>>>> 
>>>>> Unfortunately, now some records are missing again. In particular, there
are the files
>>>>> 	part-0-0, part-1-0, ..., part-10-0, part-11-0, each with corresponding
.valid-length files
>>>>> 	part-0-1, part-1-1, ..., part-10-0
>>>>> in the bucket, where job parallelism=12. So it looks to us as if one
of the files was not even created in the second attempt. This behavior seems to be what somewhat
reproducible, cf. my earlier email where the part-11 file disappeared as well.
>>>>> 
>>>>> Thanks again for your help.
>>>>> 
>>>>> Cheers,
>>>>> Max
>>>>> —
>>>>> Maximilian Bode * Junior Consultant * maximilian.bode@tngtech.com
>>>>> TNG Technology Consulting GmbH, Betastr. 13a, 85774 Unterföhring
>>>>> Geschäftsführer: Henrik Klagges, Christoph Stock, Dr. Robert Dahlke
>>>>> Sitz: Unterföhring * Amtsgericht München * HRB 135082
>>>>> 
>>>>>> Am 08.03.2016 um 11:05 schrieb Aljoscha Krettek <aljoscha@apache.org>:
>>>>>> 
>>>>>> Hi,
>>>>>> are you taking the “.valid-length” files into account. The problem
with doing “exactly-once” with HDFS is that before Hadoop 2.7 it was not possible to truncate
files. So the trick we’re using is to write the length up to which a file is valid if we
would normally need to truncate it. (If the job fails in the middle of writing the output
files have to be truncated to a valid position.) For example, say you have an output file
part-8-0. Now, if there exists a file part-8-0.valid-length this file tells you up to which
position the file part-8-0 is valid. So you should only read up to this point.
>>>>>> 
>>>>>> The name of the “.valid-length” suffix can also be configured,
by the way, as can all the other stuff.
>>>>>> 
>>>>>> If this is not the problem then I definitely have to investigate
further. I’ll also look into the Hadoop 2.4.1 build problem.
>>>>>> 
>>>>>> Cheers,
>>>>>> Aljoscha
>>>>>>> On 08 Mar 2016, at 10:26, Maximilian Bode <maximilian.bode@tngtech.com>
wrote:
>>>>>>> 
>>>>>>> Hi Aljoscha,
>>>>>>> thanks again for getting back to me. I built from your branch
and the exception is not occurring anymore. The RollingSink state can be restored.
>>>>>>> 
>>>>>>> Still, the exactly-once guarantee seems not to be fulfilled,
there are always some extra records after killing either a task manager or the job manager.
Do you have an idea where this behavior might be coming from? (I guess concrete numbers will
not help greatly as there are so many parameters influencing them. Still, in our test scenario,
we produce 2 million records in a Kafka queue but in the final output files there are on the
order of 2.1 million records, so a 5% error. The job is running in a per-job YARN session
with n=3, s=4 with a checkpointing interval of 10s.)
>>>>>>> 
>>>>>>> On another (maybe unrelated) note: when I pulled your branch,
the Travis build did not go through for -Dhadoop.version=2.4.1. I have not looked into this
further as of now, is this one of the tests known to fail sometimes?
>>>>>>> 
>>>>>>> Cheers,
>>>>>>> Max
>>>>>>> <travis.log>
>>>>>>> —
>>>>>>> Maximilian Bode * Junior Consultant * maximilian.bode@tngtech.com
>>>>>>> TNG Technology Consulting GmbH, Betastr. 13a, 85774 Unterföhring
>>>>>>> Geschäftsführer: Henrik Klagges, Christoph Stock, Dr. Robert
Dahlke
>>>>>>> Sitz: Unterföhring * Amtsgericht München * HRB 135082
>>>>>>> 
>>>>>>>> Am 07.03.2016 um 17:20 schrieb Aljoscha Krettek <aljoscha@apache.org>:
>>>>>>>> 
>>>>>>>> Hi Maximilian,
>>>>>>>> sorry for the delay, we where very busy with the release
last week. I had a hunch about the problem but I think I found a fix now. The problem is in
snapshot restore. When restoring, the sink tries to clean up any files that where previously
in progress. If Flink restores to the same snapshot twice in a row then it will try to clean
up the leftover files twice but they are not there anymore, this causes the exception.
>>>>>>>> 
>>>>>>>> I have a fix in my branch: https://github.com/aljoscha/flink/tree/rolling-sink-fix
>>>>>>>> 
>>>>>>>> Could you maybe try if this solves your problem? Which version
of Flink are you using? You would have to build from source to try it out. Alternatively I
could build it and put it onto a maven snapshot repository for you to try it out.
>>>>>>>> 
>>>>>>>> Cheers,
>>>>>>>> Aljoscha
>>>>>>>>> On 03 Mar 2016, at 14:50, Aljoscha Krettek <aljoscha@apache.org>
wrote:
>>>>>>>>> 
>>>>>>>>> Hi,
>>>>>>>>> did you check whether there are any files at your specified
HDFS output location? If yes, which files are there?
>>>>>>>>> 
>>>>>>>>> Cheers,
>>>>>>>>> Aljoscha
>>>>>>>>>> On 03 Mar 2016, at 14:29, Maximilian Bode <maximilian.bode@tngtech.com>
wrote:
>>>>>>>>>> 
>>>>>>>>>> Just for the sake of completeness: this also happens
when killing a task manager and is therefore probably unrelated to job manager HA.
>>>>>>>>>> 
>>>>>>>>>>> Am 03.03.2016 um 14:17 schrieb Maximilian Bode
<maximilian.bode@tngtech.com>:
>>>>>>>>>>> 
>>>>>>>>>>> Hi everyone,
>>>>>>>>>>> 
>>>>>>>>>>> unfortunately, I am running into another problem
trying to establish exactly once guarantees (Kafka -> Flink 1.0.0-rc3 -> HDFS).
>>>>>>>>>>> 
>>>>>>>>>>> When using
>>>>>>>>>>> 
>>>>>>>>>>> RollingSink<Tuple3<Integer,Integer,String>>
sink = new RollingSink<Tuple3<Integer,Integer,String>>("hdfs://our.machine.com:8020/hdfs/dir/outbound");
>>>>>>>>>>> sink.setBucketer(new NonRollingBucketer());
>>>>>>>>>>> output.addSink(sink);
>>>>>>>>>>> 
>>>>>>>>>>> and then killing the job manager, the new job
manager is unable to restore the old state throwing
>>>>>>>>>>> ---
>>>>>>>>>>> java.lang.Exception: Could not restore checkpointed
state to operators and functions
>>>>>>>>>>> 	at org.apache.flink.streaming.runtime.tasks.StreamTask.restoreState(StreamTask.java:454)
>>>>>>>>>>> 	at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:209)
>>>>>>>>>>> 	at org.apache.flink.runtime.taskmanager.Task.run(Task.java:559)
>>>>>>>>>>> 	at java.lang.Thread.run(Thread.java:744)
>>>>>>>>>>> Caused by: java.lang.Exception: Failed to restore
state to function: In-Progress file hdfs://our.machine.com:8020/hdfs/dir/outbound/part-5-0
was neither moved to pending nor is still in progress.
>>>>>>>>>>> 	at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.restoreState(AbstractUdfStreamOperator.java:168)
>>>>>>>>>>> 	at org.apache.flink.streaming.runtime.tasks.StreamTask.restoreState(StreamTask.java:446)
>>>>>>>>>>> 	... 3 more
>>>>>>>>>>> Caused by: java.lang.RuntimeException: In-Progress
file hdfs://our.machine.com:8020/hdfs/dir/outbound/part-5-0 was neither moved to pending nor
is still in progress.
>>>>>>>>>>> 	at org.apache.flink.streaming.connectors.fs.RollingSink.restoreState(RollingSink.java:686)
>>>>>>>>>>> 	at org.apache.flink.streaming.connectors.fs.RollingSink.restoreState(RollingSink.java:122)
>>>>>>>>>>> 	at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.restoreState(AbstractUdfStreamOperator.java:165)
>>>>>>>>>>> 	... 4 more
>>>>>>>>>>> ---
>>>>>>>>>>> I found a resolved issue [1] concerning Hadoop
2.7.1. We are in fact using 2.4.0 – might this be the same issue?
>>>>>>>>>>> 
>>>>>>>>>>> Another thing I could think of is that the job
is not configured correctly and there is some sort of timing issue. The checkpoint interval
is 10 seconds, everything else was left at default value. Then again, as the NonRollingBucketer
is used, there should not be any timing issues, right?
>>>>>>>>>>> 
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Max
>>>>>>>>>>> 
>>>>>>>>>>> [1] https://issues.apache.org/jira/browse/FLINK-2979
>>>>>>>>>>> 
>>>>>>>>>>> —
>>>>>>>>>>> Maximilian Bode * Junior Consultant * maximilian.bode@tngtech.com
>>>>>>>>>>> TNG Technology Consulting GmbH, Betastr. 13a,
85774 Unterföhring
>>>>>>>>>>> Geschäftsführer: Henrik Klagges, Christoph
Stock, Dr. Robert Dahlke
>>>>>>>>>>> Sitz: Unterföhring * Amtsgericht München *
HRB 135082
>>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>> 
>>>>>>> 
>>>>>> 
>>>>> 
>>>> 
>>> 
>> 
> 


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